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basic_api

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zhangmeng1 3 years ago
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.2.0\n",
"sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)\n",
"matplotlib 3.3.4\n",
"numpy 1.19.5\n",
"pandas 1.1.5\n",
"sklearn 0.24.2\n",
"tensorflow 2.2.0\n",
"tensorflow.keras 2.3.0-tf\n"
]
}
],
"source": [
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import sklearn\n",
"import pandas as pd\n",
"import os\n",
"import sys\n",
"import time\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow import keras\n",
"\n",
"print(tf.__version__)\n",
"print(sys.version_info)\n",
"for module in mpl, np, pd, sklearn, tf, keras:\n",
" print(module.__name__, module.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]], shape=(2, 3), dtype=float32)\n",
"--------------------------------------------------\n",
"tf.Tensor(\n",
"[[2. 3.]\n",
" [5. 6.]], shape=(2, 2), dtype=float32)\n",
"--------------------------------------------------\n",
"tf.Tensor([2. 5.], shape=(2,), dtype=float32)\n",
"--------------------------------------------------\n"
]
}
],
"source": [
"# constant是常量张量\n",
"t = tf.constant([[1., 2., 3.], [4., 5.,6.]])\n",
"\n",
"# index\n",
"#2.0能够直接获取值时因为execution默认打开的\n",
"print(t)\n",
"print('-'*50)\n",
"print(t[:, 1:])\n",
"print('-'*50)\n",
"print(t[..., 1])\n",
"print('-'*50)\n",
"# t.assign(1)对常量不能进行再次assign设置\n",
"type(t.numpy()) #转为ndarray\n",
"q=t.numpy()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
"array([[1., 2., 3.],\n",
" [4., 5., 6.]], dtype=float32)>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1= tf.constant(q) #把ndarray变为张量\n",
"t1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[11. 12. 13.]\n",
" [14. 15. 16.]], shape=(2, 3), dtype=float32)\n",
"tf.Tensor(\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]], shape=(2, 3), dtype=float32)\n",
"tf.Tensor(\n",
"[[ 1. 4. 9.]\n",
" [16. 25. 36.]], shape=(2, 3), dtype=float32)\n",
"tf.Tensor(\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]], shape=(2, 3), dtype=float32)\n",
"tf.Tensor(\n",
"[[1. 4.]\n",
" [2. 5.]\n",
" [3. 6.]], shape=(3, 2), dtype=float32)\n",
"tf.Tensor(\n",
"[[14. 32.]\n",
" [32. 77.]], shape=(2, 2), dtype=float32)\n"
]
}
],
"source": [
"# ops 使用tf本身的math接口对Tensor进行计算\n",
"print(t+10)\n",
"print(t)\n",
"print(tf.square(t))\n",
"print(t)\n",
"#矩阵乘以自己的转置\n",
"print(tf.transpose(t))\n",
"print(t @ tf.transpose(t)) #@是矩阵乘法，和*不一致"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[1. 1.4142135 1.7320508]\n",
" [2. 2.236068 2.4494898]], shape=(2, 3), dtype=float32)\n",
"--------------------------------------------------\n"
]
},
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
"array([[0. , 0.6931472, 1.0986123],\n",
" [1.3862944, 1.609438 , 1.7917595]], dtype=float32)>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(tf.sqrt(t))\n",
"print('-'*50)\n",
"# tf.math.sqrt(t)\n",
"tf.math.log(t) #必须加math"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 2. 3.]\n",
" [4. 5. 6.]]\n",
"[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]\n",
"<class 'numpy.ndarray'>\n",
"[[ 1. 4. 9.]\n",
" [16. 25. 36.]]\n",
"tf.Tensor(\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]], shape=(2, 3), dtype=float64)\n"
]
}
],
"source": [
"# numpy conversion\n",
"print(t.numpy()) #可以直接通过numpy取出来\n",
"print(t.numpy().tolist())\n",
"print(type(t.numpy()))\n",
"print(np.square(t)) #直接求平方\n",
"np_t = np.array([[1., 2., 3.], [4., 5., 6.]])\n",
"print(tf.constant(np_t)) #转换为tensor"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(2.718, shape=(), dtype=float32)\n",
"2.718\n",
"()\n"
]
}
],
"source": [
"# Scalars 就是标量，只有一个数值的张量，称为标量\n",
"t = tf.constant(2.718)\n",
"print(t)\n",
"print(t.numpy())\n",
"print(t.shape) #维数"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(b'cafe', shape=(), dtype=string)\n",
"tf.Tensor(4, shape=(), dtype=int32)\n",
"tf.Tensor(4, shape=(), dtype=int32)\n",
"tf.Tensor([ 99 97 102 101], shape=(4,), dtype=int32)\n"
]
}
],
"source": [
"# strings\n",
"t = tf.constant(\"cafe\")\n",
"print(t)\n",
"print(tf.strings.length(t))\n",
"print(tf.strings.length(t, unit=\"UTF8_CHAR\"))\n",
"print(tf.strings.unicode_decode(t, \"UTF8\"))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor([4 6 2], shape=(3,), dtype=int32)\n",
"tf.Tensor([4 6 6], shape=(3,), dtype=int32)\n",
"<tf.RaggedTensor [[99, 97, 102, 101], [99, 111, 102, 102, 101, 101], [21654, 21857]]>\n"
]
}
],
"source": [
"# string array\n",
"t = tf.constant([\"cafe\", \"coffee\", \"咖啡\"])\n",
"#自动求出数组中每一个字符的长度,如果不加unit=\"UTF8_CHAR\"，得到的是实际字节存储的长度\n",
"print(tf.strings.length(t, unit=\"UTF8_CHAR\")) \n",
"print(tf.strings.length(t, unit=\"BYTE\")) \n",
"r = tf.strings.unicode_decode(t, \"UTF8\")\n",
"# https://tool.chinaz.com/tools/unicode.aspx 汉字转的是unicode编码\n",
"print(r)\n",
"# RaggedTensor 是指形状分布不固定的（行元素个数不相等）\n",
"# Tensor，2.0新增"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.RaggedTensor [[11, 12], [21, 22, 23], [], [41]]>\n",
"(4, None)\n",
"tf.Tensor([21 22 23], shape=(3,), dtype=int32)\n",
"<tf.RaggedTensor [[21, 22, 23], []]>\n"
]
}
],
"source": [
"# ragged tensor\n",
"r = tf.ragged.constant([[11, 12], [21, 22, 23], [], [41]])\n",
"\n",
"# index op\n",
"print(r)\n",
"print(r.shape)\n",
"print(r[1])\n",
"#取一行也是ragged tensor\n",
"print(r[1:3])\n",
"# print(r[:,1])#不能取列索引"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.RaggedTensor [[11, 12], [21, 22, 23], [], [41], [51, 52], [], [], [71]]>\n",
"<tf.RaggedTensor [[11, 12, 51, 52], [21, 22, 23], [], [41, 71]]>\n"
]
}
],
"source": [
"# ops on ragged tensor\n",
"r2 = tf.ragged.constant([[51, 52],[], [], [71]])\n",
"print(tf.concat([r, r2], axis = 0))\n",
"print(tf.concat([r, r2], axis = 1)) #行数不相等，不可以拼\n",
"#是否可以把上面的axis改为1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.RaggedTensor [[11, 12, 13, 14], [21, 22, 23, 15], [41], [41, 42, 43]]>\n"
]
}
],
"source": [
"#按轴1进行合并时，行数要一致，行数相等，可以拼\n",
"r3 = tf.ragged.constant([[13, 14], [15], [41], [42, 43]])\n",
"print(tf.concat([r, r3], axis = 1))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[11 12 0]\n",
" [21 22 23]\n",
" [ 0 0 0]\n",
" [41 0 0]], shape=(4, 3), dtype=int32)\n"
]
}
],
"source": [
"print(r.to_tensor()) #各种深度学习模型必须输入一个tensor\n",
"#空闲的补0，只能往后面补"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SparseTensor(indices=tf.Tensor(\n",
"[[0 1]\n",
" [1 0]\n",
" [2 3]\n",
" [3 2]], shape=(4, 2), dtype=int64), values=tf.Tensor([1. 2. 3. 5.], shape=(4,), dtype=float32), dense_shape=tf.Tensor([4 4], shape=(2,), dtype=int64))\n"
]
},
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(4, 4), dtype=float32, numpy=\n",
"array([[0., 1., 0., 0.],\n",
" [2., 0., 0., 0.],\n",
" [0., 0., 0., 3.],\n",
" [0., 0., 5., 0.]], dtype=float32)>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sparse tensor 可以往前面补零,sparse tensor从第一行依次往下填位置\n",
"#sparese tensor存储节省内存空间，磁盘空间\n",
"s = tf.SparseTensor(indices = [[0, 1], [1, 0], [2, 3],[3,2]], #位置\n",
" values = [1., 2., 3.,5], #值\n",
" dense_shape = [4, 4]) #维数\n",
"print(s)\n",
"tt=tf.sparse.to_dense(s)\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SparseTensor(indices=tf.Tensor(\n",
"[[0 1]\n",
" [1 0]\n",
" [2 3]\n",
" [3 2]], shape=(4, 2), dtype=int64), values=tf.Tensor([ 2. 4. 6. 10.], shape=(4,), dtype=float32), dense_shape=tf.Tensor([4 4], shape=(2,), dtype=int64))\n",
"unsupported operand type(s) for +: 'SparseTensor' and 'int'\n",
"tf.Tensor(\n",
"[[ 30. 40.]\n",
" [ 20. 40.]\n",
" [210. 240.]\n",
" [250. 300.]], shape=(4, 2), dtype=float32)\n"
]
}
],
"source": [
"# ops on sparse tensors\n",
"\n",
"s2 = s * 2.0\n",
"print(s2)\n",
"\n",
"#不支持加法\n",
"try:\n",
" s3 = s + 1\n",
"except TypeError as ex:\n",
" print(ex)\n",
"\n",
"s4 = tf.constant([[10., 20.],\n",
" [30., 40.],\n",
" [50., 60.],\n",
" [70., 80.]])\n",
"# tf.sparse.to_dense(s)@s4\n",
"print(tf.sparse.sparse_dense_matmul(s, s4)) #稀疏Tensor和Tensor想乘"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SparseTensor(indices=tf.Tensor(\n",
"[[0 2]\n",
" [2 3]\n",
" [0 1]], shape=(3, 2), dtype=int64), values=tf.Tensor([1. 2. 3.], shape=(3,), dtype=float32), dense_shape=tf.Tensor([3 4], shape=(2,), dtype=int64))\n",
"SparseTensor(indices=tf.Tensor(\n",
"[[0 1]\n",
" [0 2]\n",
" [2 3]], shape=(3, 2), dtype=int64), values=tf.Tensor([3. 1. 2.], shape=(3,), dtype=float32), dense_shape=tf.Tensor([3 4], shape=(2,), dtype=int64))\n",
"tf.Tensor(\n",
"[[0. 3. 1. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 2.]], shape=(3, 4), dtype=float32)\n"
]
}
],
"source": [
"# sparse tensor\n",
"s5 = tf.SparseTensor(indices = [[0, 2], [2, 3], [0, 1]],\n",
" values = [1., 2., 3.],\n",
" dense_shape = [3, 4])\n",
"# print(tf.sparse.to_dense(s5)) #sparse无顺序时，不能转为tensor，会报错\n",
"print(s5)\n",
"s6 = tf.sparse.reorder(s5)\n",
"print(s6)\n",
"print(tf.sparse.to_dense(s6))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
"array([[1., 2., 3.],\n",
" [4., 5., 6.]], dtype=float32)>\n",
"tf.Tensor(\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]], shape=(2, 3), dtype=float32)\n",
"--------------------------------------------------\n",
"[[1. 2. 3.]\n",
" [4. 5. 6.]]\n"
]
}
],
"source": [
"# Variables\n",
"v = tf.Variable([[1., 2., 3.], [4., 5.,6.]])\n",
"print(v)\n",
"print(v.value())\n",
"print('-'*50)\n",
"print(v.numpy())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"140353908649208\n",
"140353908649208\n",
"[[ 2. 4. 6.]\n",
" [ 8. 10. 12.]]\n",
"--------------------------------------------------\n",
"[[ 2. 42. 6.]\n",
" [ 8. 10. 12.]]\n",
"--------------------------------------------------\n",
"[[ 2. 42. 6.]\n",
" [ 7. 8. 9.]]\n",
"140353908649208\n"
]
}
],
"source": [
"# 修改变量时要用assign，改变tensor内某个值，空间没有发生变化，效率高\n",
"# assign value\n",
"print(id(v))\n",
"v.assign(2*v)\n",
"print(id(v))\n",
"print(v.numpy())\n",
"print('-'*50)\n",
"v[0, 1].assign(42) #取某个元素修改\n",
"print(v.numpy())\n",
"print('-'*50)\n",
"v[1].assign([7., 8., 9.]) #取某一行修改\n",
"print(v.numpy())\n",
"print(id(v))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'ResourceVariable' object does not support item assignment\n"
]
}
],
"source": [
"try:\n",
" v[1] = [7., 8., 9.]\n",
"except TypeError as ex:\n",
" print(ex)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tf.Tensor(\n",
"[[ 4. 84. 12.]\n",
" [14. 16. 18.]], shape=(2, 3), dtype=float32)\n",
"140353905381784\n",
"<class 'tensorflow.python.framework.ops.EagerTensor'>\n"
]
}
],
"source": [
"v=2*v\n",
"print(v)\n",
"print(id(v))\n",
"print(type(v))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = tf.constant([[1., 1.], [2., 2.]])\n",
"tf.reduce_mean(x,axis=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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